Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations9357
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory128.0 B

Variable types

DateTime1
Categorical1
Text5
Numeric8

Alerts

NO2(GT) is highly overall correlated with NOx(GT) and 1 other fieldsHigh correlation
NOx(GT) is highly overall correlated with NO2(GT) and 3 other fieldsHigh correlation
PT08.S1(CO) is highly overall correlated with NOx(GT) and 4 other fieldsHigh correlation
PT08.S2(NMHC) is highly overall correlated with PT08.S1(CO) and 3 other fieldsHigh correlation
PT08.S3(NOx) is highly overall correlated with NO2(GT) and 4 other fieldsHigh correlation
PT08.S4(NO2) is highly overall correlated with PT08.S1(CO) and 2 other fieldsHigh correlation
PT08.S5(O3) is highly overall correlated with NOx(GT) and 4 other fieldsHigh correlation
Time is uniformly distributedUniform

Reproduction

Analysis started2025-11-23 13:21:16.272718
Analysis finished2025-11-23 13:21:22.933394
Duration6.66 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Date
Date

Distinct391
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
Minimum2004-01-04 00:00:00
Maximum2005-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T14:21:23.000020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:23.129595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Categorical

Uniform 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
18.00.00
 
390
19.00.00
 
390
20.00.00
 
390
21.00.00
 
390
22.00.00
 
390
Other values (19)
7407 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters74856
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.00.00
2nd row19.00.00
3rd row20.00.00
4th row21.00.00
5th row22.00.00

Common Values

ValueCountFrequency (%)
18.00.00390
 
4.2%
19.00.00390
 
4.2%
20.00.00390
 
4.2%
21.00.00390
 
4.2%
22.00.00390
 
4.2%
23.00.00390
 
4.2%
00.00.00390
 
4.2%
01.00.00390
 
4.2%
02.00.00390
 
4.2%
03.00.00390
 
4.2%
Other values (14)5457
58.3%

Length

2025-11-23T14:21:23.232741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18.00.00390
 
4.2%
19.00.00390
 
4.2%
20.00.00390
 
4.2%
21.00.00390
 
4.2%
22.00.00390
 
4.2%
23.00.00390
 
4.2%
00.00.00390
 
4.2%
01.00.00390
 
4.2%
02.00.00390
 
4.2%
03.00.00390
 
4.2%
Other values (14)5457
58.3%

Most occurring characters

ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)74856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)74856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)74856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
042498
56.8%
.18714
25.0%
15067
 
6.8%
22730
 
3.6%
31170
 
1.6%
8780
 
1.0%
9780
 
1.0%
4780
 
1.0%
5779
 
1.0%
6779
 
1.0%

CO(GT)
Text

Distinct104
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
2025-11-23T14:21:23.402963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0833601
Min length1

Characters and Unicode

Total characters28851
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row2,6
2nd row2
3rd row2,2
4th row2,2
5th row1,6
ValueCountFrequency (%)
2001592
 
17.0%
1,4279
 
3.0%
1,6275
 
2.9%
1,5273
 
2.9%
1,1262
 
2.8%
0,7260
 
2.8%
1,7258
 
2.8%
1,3253
 
2.7%
0,8251
 
2.7%
0,9248
 
2.7%
Other values (94)5406
57.8%
2025-11-23T14:21:23.663476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)28851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,7220
25.0%
05240
18.2%
24096
14.2%
13316
11.5%
31685
 
5.8%
-1683
 
5.8%
41315
 
4.6%
51044
 
3.6%
6959
 
3.3%
7823
 
2.9%
Other values (2)1470
 
5.1%

PT08.S1(CO)
Real number (ℝ)

High correlation 

Distinct1042
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1048.9901
Minimum-200
Maximum2040
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size146.2 KiB
2025-11-23T14:21:23.774584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile746
Q1921
median1053
Q31221
95-th percentile1502
Maximum2040
Range2240
Interquartile range (IQR)300

Descriptive statistics

Standard deviation329.83271
Coefficient of variation (CV)0.31442882
Kurtosis5.8369357
Mean1048.9901
Median Absolute Deviation (MAD)147
Skewness-1.7215034
Sum9815400
Variance108789.62
MonotonicityNot monotonic
2025-11-23T14:21:23.883759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200366
 
3.9%
97330
 
0.3%
110028
 
0.3%
92526
 
0.3%
96926
 
0.3%
98826
 
0.3%
93826
 
0.3%
96625
 
0.3%
98725
 
0.3%
105325
 
0.3%
Other values (1032)8754
93.6%
ValueCountFrequency (%)
-200366
3.9%
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
 
< 0.1%
6691
 
< 0.1%
6761
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6811
 
< 0.1%
ValueCountFrequency (%)
20401
< 0.1%
20081
< 0.1%
19821
< 0.1%
19751
< 0.1%
19731
< 0.1%
19611
< 0.1%
19561
< 0.1%
19341
< 0.1%
19181
< 0.1%
19171
< 0.1%

NMHC(GT)
Real number (ℝ)

Distinct430
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-159.09009
Minimum-200
Maximum1189
Zeros0
Zeros (%)0.0%
Negative8443
Negative (%)90.2%
Memory size146.2 KiB
2025-11-23T14:21:23.999351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q1-200
median-200
Q3-200
95-th percentile144.2
Maximum1189
Range1389
Interquartile range (IQR)0

Descriptive statistics

Standard deviation139.78909
Coefficient of variation (CV)-0.87867881
Kurtosis18.863824
Mean-159.09009
Median Absolute Deviation (MAD)0
Skewness4.0757845
Sum-1488606
Variance19540.99
MonotonicityNot monotonic
2025-11-23T14:21:24.351282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2008443
90.2%
6614
 
0.1%
409
 
0.1%
299
 
0.1%
888
 
0.1%
938
 
0.1%
957
 
0.1%
847
 
0.1%
577
 
0.1%
557
 
0.1%
Other values (420)838
 
9.0%
ValueCountFrequency (%)
-2008443
90.2%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
142
 
< 0.1%
161
 
< 0.1%
174
 
< 0.1%
182
 
< 0.1%
ValueCountFrequency (%)
11891
< 0.1%
11291
< 0.1%
10841
< 0.1%
10421
< 0.1%
9741
< 0.1%
9261
< 0.1%
8991
< 0.1%
8801
< 0.1%
8721
< 0.1%
8401
< 0.1%
Distinct408
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
2025-11-23T14:21:24.690607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.5084963
Min length3

Characters and Unicode

Total characters32829
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)0.7%

Sample

1st row11,9
2nd row9,4
3rd row9,0
4th row9,2
5th row6,5
ValueCountFrequency (%)
200,0366
 
3.9%
3,684
 
0.9%
2,882
 
0.9%
3,879
 
0.8%
4,078
 
0.8%
3,177
 
0.8%
3,076
 
0.8%
2,575
 
0.8%
2,973
 
0.8%
5,472
 
0.8%
Other values (398)8295
88.7%
2025-11-23T14:21:25.115326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)32829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,9357
28.5%
14513
13.7%
23117
 
9.5%
02823
 
8.6%
32159
 
6.6%
41933
 
5.9%
51874
 
5.7%
61830
 
5.6%
71700
 
5.2%
81626
 
5.0%
Other values (2)1897
 
5.8%

PT08.S2(NMHC)
Real number (ℝ)

High correlation 

Distinct1246
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean894.59528
Minimum-200
Maximum2214
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size146.2 KiB
2025-11-23T14:21:25.230029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile471
Q1711
median895
Q31105
95-th percentile1415
Maximum2214
Range2414
Interquartile range (IQR)394

Descriptive statistics

Standard deviation342.33325
Coefficient of variation (CV)0.3826683
Kurtosis2.3700888
Mean894.59528
Median Absolute Deviation (MAD)195
Skewness-0.79343464
Sum8370728
Variance117192.06
MonotonicityNot monotonic
2025-11-23T14:21:25.342613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200366
 
3.9%
85325
 
0.3%
85923
 
0.2%
80023
 
0.2%
88023
 
0.2%
98522
 
0.2%
85021
 
0.2%
78321
 
0.2%
77621
 
0.2%
76921
 
0.2%
Other values (1236)8791
94.0%
ValueCountFrequency (%)
-200366
3.9%
3832
 
< 0.1%
3871
 
< 0.1%
3881
 
< 0.1%
3902
 
< 0.1%
3971
 
< 0.1%
3991
 
< 0.1%
4022
 
< 0.1%
4072
 
< 0.1%
4081
 
< 0.1%
ValueCountFrequency (%)
22141
< 0.1%
20071
< 0.1%
19831
< 0.1%
19811
< 0.1%
19801
< 0.1%
19591
< 0.1%
19581
< 0.1%
19351
< 0.1%
19241
< 0.1%
19201
< 0.1%

NOx(GT)
Real number (ℝ)

High correlation 

Distinct926
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.61697
Minimum-200
Maximum1479
Zeros0
Zeros (%)0.0%
Negative1639
Negative (%)17.5%
Memory size146.2 KiB
2025-11-23T14:21:25.453384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q150
median141
Q3284
95-th percentile653.2
Maximum1479
Range1679
Interquartile range (IQR)234

Descriptive statistics

Standard deviation257.43387
Coefficient of variation (CV)1.5267376
Kurtosis1.5054171
Mean168.61697
Median Absolute Deviation (MAD)109
Skewness0.82523219
Sum1577749
Variance66272.196
MonotonicityNot monotonic
2025-11-23T14:21:25.607857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2001639
 
17.5%
8941
 
0.4%
6537
 
0.4%
4136
 
0.4%
12236
 
0.4%
9336
 
0.4%
13235
 
0.4%
9535
 
0.4%
18035
 
0.4%
5134
 
0.4%
Other values (916)7393
79.0%
ValueCountFrequency (%)
-2001639
17.5%
21
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
103
 
< 0.1%
114
 
< 0.1%
124
 
< 0.1%
ValueCountFrequency (%)
14791
< 0.1%
13892
< 0.1%
13691
< 0.1%
13581
< 0.1%
13451
< 0.1%
13101
< 0.1%
13011
< 0.1%
12901
< 0.1%
12531
< 0.1%
12471
< 0.1%

PT08.S3(NOx)
Real number (ℝ)

High correlation 

Distinct1222
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.99017
Minimum-200
Maximum2683
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size146.2 KiB
2025-11-23T14:21:25.725586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile410
Q1637
median794
Q3960
95-th percentile1281.2
Maximum2683
Range2883
Interquartile range (IQR)323

Descriptive statistics

Standard deviation321.99355
Coefficient of variation (CV)0.40502834
Kurtosis3.1048259
Mean794.99017
Median Absolute Deviation (MAD)161
Skewness-0.38475977
Sum7438723
Variance103679.85
MonotonicityNot monotonic
2025-11-23T14:21:25.836495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200366
 
3.9%
76725
 
0.3%
84625
 
0.3%
73325
 
0.3%
87623
 
0.2%
76523
 
0.2%
83022
 
0.2%
68522
 
0.2%
87222
 
0.2%
89122
 
0.2%
Other values (1212)8782
93.9%
ValueCountFrequency (%)
-200366
3.9%
3221
 
< 0.1%
3252
 
< 0.1%
3281
 
< 0.1%
3302
 
< 0.1%
3341
 
< 0.1%
3351
 
< 0.1%
3402
 
< 0.1%
3411
 
< 0.1%
3451
 
< 0.1%
ValueCountFrequency (%)
26831
< 0.1%
25591
< 0.1%
25421
< 0.1%
23311
< 0.1%
23271
< 0.1%
23181
< 0.1%
22941
< 0.1%
21211
< 0.1%
20952
< 0.1%
20811
< 0.1%

NO2(GT)
Real number (ℝ)

High correlation 

Distinct284
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.148873
Minimum-200
Maximum340
Zeros0
Zeros (%)0.0%
Negative1642
Negative (%)17.5%
Memory size146.2 KiB
2025-11-23T14:21:25.948542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-200
Q153
median96
Q3133
95-th percentile194
Maximum340
Range540
Interquartile range (IQR)80

Descriptive statistics

Standard deviation126.94046
Coefficient of variation (CV)2.1830252
Kurtosis0.27559907
Mean58.148873
Median Absolute Deviation (MAD)40
Skewness-1.2256296
Sum544099
Variance16113.879
MonotonicityNot monotonic
2025-11-23T14:21:26.123889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2001642
 
17.5%
9778
 
0.8%
11777
 
0.8%
11977
 
0.8%
9575
 
0.8%
10175
 
0.8%
11475
 
0.8%
11074
 
0.8%
11573
 
0.8%
10772
 
0.8%
Other values (274)7039
75.2%
ValueCountFrequency (%)
-2001642
17.5%
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
3401
< 0.1%
3331
< 0.1%
3261
< 0.1%
3221
< 0.1%
3121
< 0.1%
3101
< 0.1%
3091
< 0.1%
3061
< 0.1%
3011
< 0.1%
2961
< 0.1%

PT08.S4(NO2)
Real number (ℝ)

High correlation 

Distinct1604
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1391.4796
Minimum-200
Maximum2775
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size146.2 KiB
2025-11-23T14:21:26.244771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile757
Q11185
median1446
Q31662
95-th percentile2020.2
Maximum2775
Range2975
Interquartile range (IQR)477

Descriptive statistics

Standard deviation467.21012
Coefficient of variation (CV)0.33576497
Kurtosis3.2670279
Mean1391.4796
Median Absolute Deviation (MAD)236
Skewness-1.2441099
Sum13020075
Variance218285.3
MonotonicityNot monotonic
2025-11-23T14:21:26.357492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200366
 
3.9%
148824
 
0.3%
158022
 
0.2%
153921
 
0.2%
146720
 
0.2%
163819
 
0.2%
141818
 
0.2%
149018
 
0.2%
157017
 
0.2%
151117
 
0.2%
Other values (1594)8815
94.2%
ValueCountFrequency (%)
-200366
3.9%
5511
 
< 0.1%
5591
 
< 0.1%
5611
 
< 0.1%
5791
 
< 0.1%
6011
 
< 0.1%
6021
 
< 0.1%
6051
 
< 0.1%
6211
 
< 0.1%
6371
 
< 0.1%
ValueCountFrequency (%)
27751
< 0.1%
27461
< 0.1%
26911
< 0.1%
26841
< 0.1%
26791
< 0.1%
26671
< 0.1%
26651
< 0.1%
26621
< 0.1%
26432
< 0.1%
26412
< 0.1%

PT08.S5(O3)
Real number (ℝ)

High correlation 

Distinct1744
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean975.07203
Minimum-200
Maximum2523
Zeros0
Zeros (%)0.0%
Negative366
Negative (%)3.9%
Memory size146.2 KiB
2025-11-23T14:21:26.465448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile348
Q1700
median942
Q31255
95-th percentile1750
Maximum2523
Range2723
Interquartile range (IQR)555

Descriptive statistics

Standard deviation456.93818
Coefficient of variation (CV)0.46861993
Kurtosis0.63829664
Mean975.07203
Median Absolute Deviation (MAD)272
Skewness-0.03466188
Sum9123749
Variance208792.5
MonotonicityNot monotonic
2025-11-23T14:21:26.585115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-200366
 
3.9%
83620
 
0.2%
82520
 
0.2%
82619
 
0.2%
92618
 
0.2%
79917
 
0.2%
77717
 
0.2%
90516
 
0.2%
92316
 
0.2%
89116
 
0.2%
Other values (1734)8832
94.4%
ValueCountFrequency (%)
-200366
3.9%
2211
 
< 0.1%
2251
 
< 0.1%
2271
 
< 0.1%
2321
 
< 0.1%
2521
 
< 0.1%
2531
 
< 0.1%
2571
 
< 0.1%
2612
 
< 0.1%
2621
 
< 0.1%
ValueCountFrequency (%)
25231
< 0.1%
25221
< 0.1%
25191
< 0.1%
25151
< 0.1%
24941
< 0.1%
24801
< 0.1%
24751
< 0.1%
24651
< 0.1%
24521
< 0.1%
24341
< 0.1%

T
Text

Distinct437
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
2025-11-23T14:21:26.951979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8233408
Min length3

Characters and Unicode

Total characters35775
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st row13,6
2nd row13,3
3rd row11,9
4th row11,0
5th row11,2
ValueCountFrequency (%)
200366
 
3.9%
20,857
 
0.6%
21,354
 
0.6%
20,251
 
0.5%
13,851
 
0.5%
15,649
 
0.5%
12,349
 
0.5%
12,049
 
0.5%
16,348
 
0.5%
19,848
 
0.5%
Other values (417)8535
91.2%
2025-11-23T14:21:27.399838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)35775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,8991
25.1%
15366
15.0%
24858
13.6%
32911
 
8.1%
02485
 
6.9%
41952
 
5.5%
51855
 
5.2%
61798
 
5.0%
81788
 
5.0%
71729
 
4.8%
Other values (2)2042
 
5.7%

RH
Text

Distinct754
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
2025-11-23T14:21:27.777834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9992519
Min length3

Characters and Unicode

Total characters37421
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st row48,9
2nd row47,7
3rd row54,0
4th row60,0
5th row59,6
ValueCountFrequency (%)
200366
 
3.9%
53,131
 
0.3%
57,930
 
0.3%
47,830
 
0.3%
60,827
 
0.3%
45,927
 
0.3%
49,826
 
0.3%
50,926
 
0.3%
43,426
 
0.3%
50,826
 
0.3%
Other values (744)8742
93.4%
2025-11-23T14:21:28.284079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)37421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,8991
24.0%
43578
 
9.6%
53499
 
9.4%
33316
 
8.9%
63257
 
8.7%
23000
 
8.0%
72630
 
7.0%
02569
 
6.9%
82194
 
5.9%
12140
 
5.7%
Other values (2)2247
 
6.0%

AH
Text

Distinct6684
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
2025-11-23T14:21:28.601071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9217698
Min length4

Characters and Unicode

Total characters55410
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4872 ?
Unique (%)52.1%

Sample

1st row0,7578
2nd row0,7255
3rd row0,7502
4th row0,7867
5th row0,7888
ValueCountFrequency (%)
200366
 
3.9%
1,11996
 
0.1%
0,83946
 
0.1%
0,96846
 
0.1%
0,74876
 
0.1%
0,97226
 
0.1%
1,05945
 
0.1%
0,87365
 
0.1%
0,92715
 
0.1%
0,66865
 
0.1%
Other values (6674)8941
95.6%
2025-11-23T14:21:29.019006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)55410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
,8991
16.2%
08868
16.0%
17744
14.0%
43831
6.9%
23821
6.9%
93804
6.9%
83659
6.6%
73645
6.6%
63578
 
6.5%
33552
 
6.4%
Other values (2)3917
7.1%

Interactions

2025-11-23T14:21:21.969794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:16.814838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.462576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.511418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.150332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.835930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.499823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.321963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.066695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:16.893675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.544633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.587447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.230706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.913745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.723577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.400422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.153671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:16.977470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.633503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.668471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.318739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.000084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.812257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.483711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.234152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.073441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.075716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.741444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.402869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.089382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.890923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.560887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.320832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.157776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.168301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.827604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.489779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.174634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.977308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.645304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.402014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.234475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.248539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.902147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.577158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.253623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.076104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.729508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.486174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.310297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.333117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.980124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.664095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.333236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.157932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.810060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:22.595206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:17.383383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:18.420317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.073349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:19.750095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:20.415942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.241491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T14:21:21.887201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-23T14:21:29.126921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
NMHC(GT)NO2(GT)NOx(GT)PT08.S1(CO)PT08.S2(NMHC)PT08.S3(NOx)PT08.S4(NO2)PT08.S5(O3)Time
NMHC(GT)1.0000.022-0.0380.1320.0280.1610.1200.0250.082
NO2(GT)0.0221.0000.9060.4760.457-0.5220.0610.4980.262
NOx(GT)-0.0380.9061.0000.5070.480-0.5810.0660.5510.197
PT08.S1(CO)0.1320.4760.5071.0000.902-0.6450.6860.9060.222
PT08.S2(NMHC)0.0280.4570.4800.9021.000-0.6420.7770.8880.257
PT08.S3(NOx)0.161-0.522-0.581-0.645-0.6421.000-0.363-0.6520.176
PT08.S4(NO2)0.1200.0610.0660.6860.777-0.3631.0000.6100.174
PT08.S5(O3)0.0250.4980.5510.9060.888-0.6520.6101.0000.166
Time0.0820.2620.1970.2220.2570.1760.1740.1661.000

Missing values

2025-11-23T14:21:22.728471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-23T14:21:22.852880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH
010/03/200418.00.002,61360.0150.011,91046.0166.01056.0113.01692.01268.013,648,90,7578
110/03/200419.00.0021292.0112.09,4955.0103.01174.092.01559.0972.013,347,70,7255
210/03/200420.00.002,21402.088.09,0939.0131.01140.0114.01555.01074.011,954,00,7502
310/03/200421.00.002,21376.080.09,2948.0172.01092.0122.01584.01203.011,060,00,7867
410/03/200422.00.001,61272.051.06,5836.0131.01205.0116.01490.01110.011,259,60,7888
510/03/200423.00.001,21197.038.04,7750.089.01337.096.01393.0949.011,259,20,7848
611/03/200400.00.001,21185.031.03,6690.062.01462.077.01333.0733.011,356,80,7603
711/03/200401.00.0011136.031.03,3672.062.01453.076.01333.0730.010,760,00,7702
811/03/200402.00.000,91094.024.02,3609.045.01579.060.01276.0620.010,759,70,7648
911/03/200403.00.000,61010.019.01,7561.0-200.01705.0-200.01235.0501.010,360,20,7517
DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAH
934704/04/200505.00.000,5888.0-200.01,3528.077.01077.053.0987.0578.010,459,90,7550
934804/04/200506.00.001,11031.0-200.04,4730.0182.0760.093.01129.0905.09,563,10,7531
934904/04/200507.00.004,01384.0-200.017,41221.0594.0470.0155.01600.01457.09,761,90,7446
935004/04/200508.00.005,01446.0-200.022,41362.0586.0415.0174.01777.01705.013,548,90,7553
935104/04/200509.00.003,91297.0-200.013,61102.0523.0507.0187.01375.01583.018,236,30,7487
935204/04/200510.00.003,11314.0-200.013,51101.0472.0539.0190.01374.01729.021,929,30,7568
935304/04/200511.00.002,41163.0-200.011,41027.0353.0604.0179.01264.01269.024,323,70,7119
935404/04/200512.00.002,41142.0-200.012,41063.0293.0603.0175.01241.01092.026,918,30,6406
935504/04/200513.00.002,11003.0-200.09,5961.0235.0702.0156.01041.0770.028,313,50,5139
935604/04/200514.00.002,21071.0-200.011,91047.0265.0654.0168.01129.0816.028,513,10,5028